code: def test_scatter_all_embeddings_single_input(self):
      glue_model = GlueModelForTesting(
          model_name_or_path="bert-base-uncased",
          text_a_name="sentence1",
      )
      emb_size = 10
      # We'll inject zeros for the embeddings of 'hi',
      # while special tokens get vectors of 1s.
      embs_a = np.zeros((1, emb_size))
      input_embs = np.ones((1, 3, emb_size))
      # Scatter embs_a into input_embs
      result = glue_model.scatter_all_embeddings(
          [
              {
                  "sentence1": "hi",
                  "input_embs_sentence1": embs_a,
              }
          ],
          input_embs,
      )
      target = [
          [
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
          ]
      ]
      np.testing.assert_almost_equal(result, target)
  
file_path: lit/lit_nlp/examples/models/glue_models_test.py
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import attr
  from lit_nlp.examples.models import glue_models
  import numpy
label_desc: Use image node_id 2 for calling the GlueModelsTest obj's underlying member method code with example usage: obj.test_scatter_all_embeddings_single_input() without return types